<p>Tropical floodplain forests are among the most complex ecosystem on earth, featured by vegetation adapted to survive in seasonal flood environments. Although their ability to resist the periodic water level oscillations, recent studies have shown that riparian forests are extremely sensitive to long-term hydrological changes caused by both anthropogenic and natural disturbances. During the recent decades fragmentation and regulation of rivers induced severe alterations of natural &#8220;flood pulse&#8221; and sediment supply along the whole watercourse, causing massive tree mortality and compromising seeds spreading. The hydroclimatic anomalies of El Nino/Southern Oscillation (ENSO) and climate change impact on riparian environments, aggravating forest stress and vulnerability to fires, in cases of prolonged drought, while inducing tree mortality for anoxia, when a multi-year uninterrupted flood occurred.</p><p>In order to develop future solutions to mitigate the consequences of these disturbances and to enable a sustainable and effective management of riparian forests in the aquatic-terrestrial transitional zone (ATTZ), large-scale monitoring of these areas is necessary. Mapping and monitoring of floodplain vegetation are extremely important not only to assess vegetation status but also because vegetation represents an indicator for early signs of any physical or chemical environmental degradation. Remote sensing offers practical and efficient techniques to estimate biochemical and biophysical parameters and analyse their evolution over time even for very remote and poor accessible areas such as tropical floodplains. Nevertheless, as the main vegetation dynamics are in the narrow area at the interface terrestrial and aquatic systems, a high spatial and temporal resolution of the data is needed for their analysis. Furthermore, the extreme cloudiness of tropical regions contaminates the land surface observation causing gap in the data.</p><p>In the present study, we combine Landsat (30m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500m spatial resolution and daily revisit cycle), using HISTARFM algorithm, to reduce noise and produce monthly gap-free high-resolution (30 m) observations over land and the associated estimation of uncertainties. Subsequently, high resolution maps of normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were derived from the high-resolution gap free reflectance data. Furthermore, estimation of NDVI and EVI uncertainties was calculated through an error propagation analyses from uncertainties of reflectance estimates.</p><p>The framework we developed has been used to derive high resolution mapping of floodplain vegetation in the large tropical rivers that during the last decades experimented a hydrological regime transition. In a first-phase, vegetation dynamic analysis focused of the tropical large rivers in Amazonia and preliminary results of the temporal series will be presented.</p><p>The coupling of hydro-geomorphological and vegetation data enables the monitoring of riparian vegetation dynamics and a better understanding of the impact that the human footprint and climate change have on them.</p>
Hydrological Climate-Impact Projections for the Rhine River: GCM–RCM Uncertainty and Separate Temperature and Precipitation Effects*
